Watershed vs Workiva
One of fourteen models named Watershed first on the direct prompt; two named Workiva. Watershed was named by fourteen of the fourteen models and Workiva by thirteen and both carry 30 labels, so the shares below are directly comparable.
Named in one category this edition.
Named in eleven categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the ESG and carbon reporting page.
By framing
WatershedFirst choices, of fourteen modelsWorkiva
Across every category in the October 2026 Edition, Watershed and Workiva were named in the same answer fifty-two times, of the 84 answers naming Watershed and the 625 naming Workiva. In those answers Workiva took the first choice nineteen times and Watershed three.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | |||||
| GPT-5.4 mini | | | | |||
| Gemini 3.5 Flash | | | | | | |
| Perplexity Sonar | | | ||||
| Grok 4.1 Fast | | | | | | |
| Mistral Small | ||||||
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | | | | |||
| Kimi K2 | | | | |||
| GLM 4.7 FlashX | | | ||||
| MiniMax M2.5 | | | | |||
| GPT-6 Luna | | | | |||
| Muse Glimmer 30B | |
Watershed Workiva first choice named as an alternative argued againstblank: not namedEach cell is one answer, Watershed on the left and Workiva on the right.The direct prompt
Watershed first, Workiva not the choice
1 of 14 modelsWorkiva was named in the answer but not as the choice, or not at all.Workiva first, Watershed not the choice
2 of 14 modelsWatershed was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
2 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
9 of 14 modelsThe answer made no first choice from these two in this category.Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
Watershed3%#5 of 13Workiva3%#8 of 13What the models said about Watershed
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“What to Avoid - Watershed — Excellent but typically over $50K–$250K/year; better suited for large enterprises” Kimi K2 · paraphrase prompt · hard negative
“rather than self-serve platforms (like Watershed, which assumes you have an in-house expert to run the strategy)” Gemini 3.5 Flash · direct prompt · soft negative
“built for large corporations and carry five-figure price tags and long sales cycles” Gemini 3.5 Flash · budget prompt · soft negative
“Choose Watershed or Persefoni if: Your primary immediate pain point is calculating Scope 1, 2, and 3 emissions accurately” Qwen 3.7 Flash · comparative prompt · first choice
“It is the current market leader for mid-market to enterprise. It excels at automating carbon accounting” Qwen 3.7 Flash · direct prompt · first choice
“I'd suggest focusing on vendors like Greenly, Novisto, Tracera, or Watershed” MiniMax M2.5 · scale prompt · first choice
What the models said about Workiva
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Avoid paying for enterprise platforms like Workiva unless you have complex disclosure, assurance, or multi-framework requirements” Perplexity Sonar · budget prompt · hard negative
“Avoid Workiva unless you're already a public company or have a dedicated compliance team with a six-figure software budget.” DeepSeek V4 Flash · direct prompt · hard negative
“ISG's 2025 Buyers Guide named Workiva, Salesforce, and Oracle the overall leaders... A strong candidate for public companies and assurance-focused teams.” GPT-6 Luna · comparative prompt · first choice
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.